Heterogeneity in Statistical Genetics - How to Assess, Address, and Account for Mixtures in Association Studies (Paperback, 1st ed. 2020)

Heterogeneity, or mixtures, are ubiquitous in genetics. Even for data as simple as mono-genic diseases, populations are a mixture of affected and unaffected individuals. Still, most statistical genetic association analyses, designed to map genes for diseases and other genetic traits, ignore this phenomenon. In this book, we document methods that incorporate heterogeneity into the design and analysis of genetic and genomic association data. Among the key qualities of our developed statistics is that they include mixture parameters as part of the statistic, a unique component for tests of association. A critical feature of this work is the inclusion of at least one heterogeneity parameter when performing statistical power and sample size calculations for tests of genetic association. We anticipate that this book will be useful to researchers who want to estimate heterogeneity in their data, develop or apply genetic association statistics where heterogeneity exists, and accurately evaluate statistical power and sample size for genetic association through the application of robust experimental design.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Statistics for Biology and Health
Release date:	December 2021
First published:	2020
Authors:	Derek Gordon • Stephen J. Finch • Wonkuk Kim
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	352
Edition:	1st ed. 2020
ISBN-13:	978-3-03-061123-1
Categories:	Books > Medicine > Pre-clinical medicine: basic sciences > Medical genetics Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Genetics (non-medical) > General
LSN:	3-03-061123-X
Barcode:	9783030611231

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